This experiment demonstrates the use of cross validation in binary classification.

In this experiment, with the help of cross-validate module, we will decide which binary classifier algorithm to use for our dataset (adult census income binary dataset). This is an extension of the experiment **Sample 3 : Cross Validation for Binary Classification: Adult Dataset**. You can find the details of the experiment [here][1].
We will compare between four different binary classifiers as shown below.
<img src="https://raw.githubusercontent.com/shaheeng/ShaheenGauher/master/Images/sample3extendedExp.PNG" width="600" height="300" />
Using R script we can view the metrics from each classifier and select which classifier would perform the best. The output is shown below.
<img src="https://raw.githubusercontent.com/shaheeng/ShaheenGauher/master/Images/sample3crossvalidation.png" width="600" height="300" />
Fig. Comparison of four binary classifiers using cross validation.
Based on the results above, for maximum accuracy we would select Boosted Trees.
[1]: https://gallery.cortanaanalytics.com/Experiment/d9d3c11b360343be9c9cbdc6a3981350